

from __future__ import annotations

__snake_case				:      Optional[Any]   		=			tuple[int, int, int]
__snake_case				:      Tuple   		=			tuple[str, str, str]


# used alphabet --------------------------
# from string.ascii_uppercase
__snake_case				:      int   		=			"""ABCDEFGHIJKLMNOPQRSTUVWXYZ"""

# -------------------------- default selection --------------------------
# rotors --------------------------
__snake_case				:      int   		=			"""EGZWVONAHDCLFQMSIPJBYUKXTR"""
__snake_case				:      List[Any]   		=			"""FOBHMDKEXQNRAULPGSJVTYICZW"""
__snake_case				:      str   		=			"""ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
__snake_case				:      int   		=			{
    """A""": """N""",
    """N""": """A""",
    """B""": """O""",
    """O""": """B""",
    """C""": """P""",
    """P""": """C""",
    """D""": """Q""",
    """Q""": """D""",
    """E""": """R""",
    """R""": """E""",
    """F""": """S""",
    """S""": """F""",
    """G""": """T""",
    """T""": """G""",
    """H""": """U""",
    """U""": """H""",
    """I""": """V""",
    """V""": """I""",
    """J""": """W""",
    """W""": """J""",
    """K""": """X""",
    """X""": """K""",
    """L""": """Y""",
    """Y""": """L""",
    """M""": """Z""",
    """Z""": """M""",
}

# -------------------------- extra rotors --------------------------
__snake_case				:      int   		=			"""RMDJXFUWGISLHVTCQNKYPBEZOA"""
__snake_case				:      List[str]   		=			"""SGLCPQWZHKXAREONTFBVIYJUDM"""
__snake_case				:      Tuple   		=			"""HVSICLTYKQUBXDWAJZOMFGPREN"""
__snake_case				:      List[str]   		=			"""RZWQHFMVDBKICJLNTUXAGYPSOE"""
__snake_case				:      Tuple   		=			"""LFKIJODBEGAMQPXVUHYSTCZRWN"""
__snake_case				:      Dict   		=			"""KOAEGVDHXPQZMLFTYWJNBRCIUS"""


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__):

			'''simple docstring'''



			if (unique_rotsel := len(set(a__))) < 3:
						a_						:						Dict 							= f'''Please use 3 unique rotors (not {unique_rotsel})'''
						raise Exception(a__)

			# Checks if rotor positions are valid
			a_							,					a_							,					a_						:						List[str] 							= rotpos
			if not 0 < rotorposa <= len(a__):
						a_						:						List[str] 							= f'''First rotor position is not within range of 1..26 ({rotorposa}'''
						raise ValueError(a__)
			if not 0 < rotorposa <= len(a__):
						a_						:						int 							= f'''Second rotor position is not within range of 1..26 ({rotorposa})'''
						raise ValueError(a__)
			if not 0 < rotorposa <= len(a__):
						a_						:						int 							= f'''Third rotor position is not within range of 1..26 ({rotorposa})'''
						raise ValueError(a__)

			# Validates string and returns dict
			a_						:						Optional[int] 							= _plugboard(a__)

			return rotpos, rotsel, pbdict


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			if not isinstance(a__  ,	a__):
						a_						:						Any 							= f'''Plugboard setting isn\'t type string ({type(a__)})'''
						raise TypeError(a__)
			elif len(a__) % 2 != 0:
						a_						:						Any 							= f'''Odd number of symbols ({len(a__)})'''
						raise Exception(a__)
			elif pbstring == "":
						return {}

			pbstring.replace(""" """  ,	"""""")

			# Checks if all characters are unique
			a_						:						int 							= set()
			for i in pbstring:
						if i not in abc:
									a_						:						Any 							= f'''\'{i}\' not in list of symbols'''
									raise Exception(a__)
						elif i in tmppbl:
									a_						:						int 							= f'''Duplicate symbol ({i})'''
									raise Exception(a__)
						else:
									tmppbl.add(a__)
			del tmppbl

			# Created the dictionary
			a_						:						str 							= {}
			for j in range(0  ,	len(a__) - 1  ,	2):
						a_						:						List[str] 							= pbstring[j + 1]
						a_						:						Any 							= pbstring[j]

			return pb


def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__ = (rotora, rotora, rotora)  ,	a__ = ""  ,	):

			'''simple docstring'''



			a_						:						Any 							= text.upper()
			a_							,					a_							,					a_						:						Tuple 							= _validator(
			    a__  ,	a__  ,	plugb.upper())

			a_							,					a_							,					a_						:						Any 							= rotor_position
			a_							,					a_							,					a_						:						Tuple 							= rotor_selection
			rotorposa -= 1
			rotorposa -= 1
			rotorposa -= 1

			a_						:						Optional[int] 							= []

			# encryption/decryption process --------------------------
			for symbol in text:
						if symbol in abc:
									# 1st plugboard --------------------------
									if symbol in plugboard:
												a_						:						str 							= plugboard[symbol]

									# rotor ra --------------------------
									a_						:						str 							= abc.index(a__) + rotorposa
									a_						:						int 							= rotora[index % len(a__)]

									# rotor rb --------------------------
									a_						:						List[str] 							= abc.index(a__) + rotorposa
									a_						:						Union[str, Any] 							= rotora[index % len(a__)]

									# rotor rc --------------------------
									a_						:						Any 							= abc.index(a__) + rotorposa
									a_						:						Any 							= rotora[index % len(a__)]

									# reflector --------------------------
									# this is the reason you don't need another machine to decipher

									a_						:						Optional[Any] 							= reflector[symbol]

									# 2nd rotors
									a_						:						int 							= abc[rotora.index(a__) - rotorposa]
									a_						:						int 							= abc[rotora.index(a__) - rotorposa]
									a_						:						Optional[int] 							= abc[rotora.index(a__) - rotorposa]

									# 2nd plugboard
									if symbol in plugboard:
												a_						:						str 							= plugboard[symbol]

									# moves/resets rotor positions
									rotorposa += 1
									if rotorposa >= len(a__):
												a_						:						Optional[int] 							= 0
												rotorposa += 1
									if rotorposa >= len(a__):
												a_						:						Optional[Any] 							= 0
												rotorposa += 1
									if rotorposa >= len(a__):
												a_						:						Optional[Any] 							= 0

        # else:
        #    pass
        #    Error could be also raised
        #    raise ValueError(
        #       'Invalid symbol('+repr(symbol)+')')
						result.append(a__)

			return "".join(a__)


if __name__ == "__main__":
	__snake_case				:      List[str]   		=			"""This is my Python script that emulates the Enigma machine from WWII."""
	__snake_case				:      Dict   		=			(1, 1, 1)
	__snake_case				:      Optional[Any]   		=			"""pictures"""
	__snake_case				:      Dict   		=			(rotora, rotora, rotora)
	__snake_case				:      List[Any]   		=			enigma(message, rotor_pos, rotor_sel, pb)

	print("""Encrypted message:""", en)
	print("""Decrypted message:""", enigma(en, rotor_pos, rotor_sel, pb))





import logging

import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEncoder,
    BertModel,
    BertPreTrainedModel,
)


__snake_case				:      Optional[int]   		=			logging.getLogger(__name__)



class        A__(a_							):
						"""simple docstring"""






						def 			UpperCamelCase__					(    self  ,      _lowercase  ,      _lowercase  ,      _lowercase=None  ,      _lowercase=None    )     -> Tuple:
									a_						:						str 							= self.layer[current_layer](_lowercase  ,      _lowercase  ,      head_mask[current_layer]    )

									a_						:						str 							= layer_outputs[0]

									return hidden_states







@add_start_docstrings(
    '''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.''',  a_,  )
class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> List[str]:
									super().__init__(_lowercase    )

									a_						:						Tuple 							= BertEncoderWithPabee(_lowercase    )

									self.init_weights()
									a_						:						int 							= 0
									a_						:						Any 							= 0
									a_						:						Tuple 							= 0

									a_						:						Optional[int] 							= 0





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> Tuple:
									a_						:						Dict 							= threshold





						def 			UpperCamelCase__					(    self  ,      _lowercase    )     -> List[Any]:
									a_						:						Optional[int] 							= patience





						def 			UpperCamelCase__					(    self    )     -> Dict:
									a_						:						str 							= 0
									a_						:						Optional[int] 							= 0





						def 			UpperCamelCase__					(    self    )     -> List[Any]:
									a_						:						Union[str, Any] 							= self.inference_layers_num / self.inference_instances_num
									a_						:						Optional[int] 							= (
									    F'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
									    F''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
									)
									print(_lowercase    )





						@add_start_docstrings_to_model_forward(_lowercase    )
						def 			UpperCamelCase__					(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=False  ,      )     -> str:

									if input_ids is not None and inputs_embeds is not None:
												raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time"""    )
									elif input_ids is not None:
												a_						:						Dict 							= input_ids.size()
									elif inputs_embeds is not None:
												a_						:						Dict 							= inputs_embeds.size()[:-1]
									else:
												raise ValueError("""You have to specify either input_ids or inputs_embeds"""    )

									a_						:						Optional[Any] 							= input_ids.device if input_ids is not None else inputs_embeds.device

									if attention_mask is None:
												a_						:						Tuple 							= torch.ones(_lowercase  ,      device=_lowercase    )
									if token_type_ids is None:
												a_						:						List[str] 							= torch.zeros(_lowercase  ,      dtype=torch.long  ,      device=_lowercase    )

									# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
									# ourselves in which case we just need to make it broadcastable to all heads.
									a_						:						torch.Tensor 							= self.get_extended_attention_mask(_lowercase  ,      _lowercase  ,      _lowercase    )

									# If a 2D ou 3D attention mask is provided for the cross-attention
									# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
									if self.config.is_decoder and encoder_hidden_states is not None:
												a_							,					a_							,					a_						:						int 							= encoder_hidden_states.size()
												a_						:						List[str] 							= (encoder_batch_size, encoder_sequence_length)
												if encoder_attention_mask is None:
															a_						:						Tuple 							= torch.ones(_lowercase  ,      device=_lowercase    )
												a_						:						List[Any] 							= self.invert_attention_mask(_lowercase    )
									else:
												a_						:						Optional[Any] 							= None

									# Prepare head mask if needed
									# 1.0 in head_mask indicate we keep the head
									# attention_probs has shape bsz x n_heads x N x N
									# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
									# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
									a_						:						List[Any] 							= self.get_head_mask(_lowercase  ,      self.config.num_hidden_layers    )

									a_						:						List[str] 							= self.embeddings(
									    input_ids=_lowercase  ,      position_ids=_lowercase  ,      token_type_ids=_lowercase  ,      inputs_embeds=_lowercase    )
									a_						:						List[Any] 							= embedding_output

									if self.training:
												a_						:						Any 							= []
												for i in range(self.config.num_hidden_layers    ):
															a_						:						int 							= self.encoder.adaptive_forward(
															    _lowercase  ,      current_layer=_lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase    )

															a_						:						List[Any] 							= self.pooler(_lowercase    )
															a_						:						Optional[int] 							= output_layers[i](output_dropout(_lowercase    )    )
															res.append(_lowercase    )
									elif self.patience == 0:  # Use all layers for inference
												a_						:						Union[str, Any] 							= self.encoder(
												    _lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase  ,      encoder_hidden_states=_lowercase  ,      encoder_attention_mask=_lowercase  ,      )
												a_						:						Union[str, Any] 							= self.pooler(encoder_outputs[0]    )
												a_						:						List[str] 							= [output_layers[self.config.num_hidden_layers - 1](_lowercase    )]
									else:
												a_						:						Any 							= 0
												a_						:						Dict 							= None
												a_						:						Tuple 							= 0
												for i in range(self.config.num_hidden_layers    ):
															calculated_layer_num += 1
															a_						:						Optional[Any] 							= self.encoder.adaptive_forward(
															    _lowercase  ,      current_layer=_lowercase  ,      attention_mask=_lowercase  ,      head_mask=_lowercase    )

															a_						:						Optional[int] 							= self.pooler(_lowercase    )
															a_						:						int 							= output_layers[i](_lowercase    )
															if regression:
																		a_						:						Dict 							= logits.detach()
																		if patient_result is not None:
																					a_						:						Optional[Any] 							= patient_result.detach()
																		if (patient_result is not None) and torch.abs(patient_result - labels    ) < self.regression_threshold:
																					patient_counter += 1
																		else:
																					a_						:						Optional[Any] 							= 0
															else:
																		a_						:						str 							= logits.detach().argmax(dim=1    )
																		if patient_result is not None:
																					a_						:						str 							= patient_result.detach().argmax(dim=1    )
																		if (patient_result is not None) and torch.all(labels.eq(_lowercase    )    ):
																					patient_counter += 1
																		else:
																					a_						:						Tuple 							= 0

															a_						:						Union[str, Any] 							= logits
															if patient_counter == self.patience:
																		break
												a_						:						str 							= [patient_result]
												self.inference_layers_num += calculated_layer_num
												self.inference_instances_num += 1

									return res







@add_start_docstrings(
    '''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
    the pooled output) e.g. for GLUE tasks. ''',  a_,  )
class        A__(a_							):
						"""simple docstring"""






						def __init__(    self  ,      _lowercase    )     -> str:
									super().__init__(_lowercase    )
									a_						:						str 							= config.num_labels

									a_						:						Optional[Any] 							= BertModelWithPabee(_lowercase    )
									a_						:						int 							= nn.Dropout(config.hidden_dropout_prob    )
									a_						:						str 							= nn.ModuleList(
									    [nn.Linear(config.hidden_size  ,      self.config.num_labels    ) for _ in range(config.num_hidden_layers    )]    )

									self.init_weights()





						@add_start_docstrings_to_model_forward(_lowercase    )
						def 			UpperCamelCase__					(    self  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      _lowercase=None  ,      )     -> Tuple:
									a_						:						Optional[Any] 							= self.bert(
									    input_ids=_lowercase  ,      attention_mask=_lowercase  ,      token_type_ids=_lowercase  ,      position_ids=_lowercase  ,      head_mask=_lowercase  ,      inputs_embeds=_lowercase  ,      output_dropout=self.dropout  ,      output_layers=self.classifiers  ,      regression=self.num_labels == 1  ,      )

									a_						:						Optional[Any] 							= (logits[-1],)

									if labels is not None:
												a_						:						int 							= None
												a_						:						Union[str, Any] 							= 0
												for ix, logits_item in enumerate(_lowercase    ):
															if self.num_labels == 1:
																		#  We are doing regression
																		a_						:						Any 							= MSELoss()
																		a_						:						Union[str, Any] 							= loss_fct(logits_item.view(-1    )  ,      labels.view(-1    )    )
															else:
																		a_						:						Any 							= CrossEntropyLoss()
																		a_						:						int 							= loss_fct(logits_item.view(-1  ,      self.num_labels    )  ,      labels.view(-1    )    )
															if total_loss is None:
																		a_						:						str 							= loss
															else:
																		total_loss += loss * (ix + 1)
															total_weights += ix + 1
												a_						:						Any 							= (total_loss / total_weights,) + outputs

									return outputs


